Regions Of China Research Articles

Prokaryotic Expression of Coat Protein Gene of Grapevine Berry Inner Necrosis Virus and Preparation of Its Polyclonal Antibody

Grapevine berry inner necrosis virus (GINV) and grapevine Pinot gris virus (GPGV) are prevalent viral diseases affecting viticulture, posing significant threats in grape-producing regions of China. Previous studies have emphasized the harmful effects of grape viruses on the grape industry all over the world. However, few reports have focused specifically on GINV. In wild grapevines, GINV infection frequently leads to grapevine fanleaf degeneration disease (GFDD). GINV often co-occurs with other grape viruses, exacerbating its harmful effects on the grapevine industry in China. In this study, we collected grapevine samples from Taizhou city, Jiangsu Province, where GINV infection was confirmed. Based on the GINV coat protein (CP) gene, we developed a high-throughput and high-sensitivity direct antigen-coated ELISA and Dot blot assay for field diagnosis of GINV CP in grape samples. The CP gene was cloned from GINV-infected grape samples, and the GINV CP was expressed using the pET30(a) vector. Specific polyclonal antiserum CPGINV was generated by immunizing rabbits with the purified protein, and its sensitivity was determined to be satisfactory. Leveraging the high accuracy and sensitivity of the CPGINV antiserum, we developed a rapid, precise, and scalable diagnostic method for GINV in the grapevine industry. The established ELISA and Dot blot assays successfully detected GINV-infected grapevine samples. The occurrence of GINV is relatively common in China, which poses a risk of transmission and threatens the healthy development of the grape industry. Therefore, this study prepared CPGINV antiserum and established an efficient, rapid, sensitive, accurate, and high-throughput diagnostic method, providing a foundational approach for the prevention and control of vitis viral diseases.

Assessing Land Use and Cover Changes arising from the 2022 water crisis in Southeast China: A comparative analysis of Remote Sensing Imagery classifications and Machine Learning algorithms

Abstract. The water crisis in the southeast region of China in 2022, caused by one of the worst heatwaves on record, was characterized by severe shortages of water resources, leading to challenges for local communities, agriculture, and industry. To analyze changes in land use and land cover (LULC) in the Jialing River region, Chongqing, China, we compared Remote Sensing (RS) imagery classifications before and after the intense heat waves of 2022. We evaluated the performance of two machine learning algorithms, KDTree KNN and Random Forest (RF), in LULC classifications. The classifications were carried out based on the RS images from the OLI/Landsat 8 system, NDWI index, and SRTM data. The model performances were similar, the classification accuracy showed that the RF algorithm was superior to KDTree KNN. The RF LULC classification and area calculation corroborate with the visual analysis, reaffirming the superiority of RF, which shows a decrease in water surface area, unlike DKTree KNN.

Deformation Mechanism in Central China Revealed by Regional Earthquake Tomography

Abstract The central China region (CCN) has experienced significant deformation and complex structural arrangements. We conducted research on the deformation pattern of the CCN through the 3D velocity structure of the crust and uppermost mantle obtained via body-wave tomography. This velocity structure strongly correlates with the region’s topographic features. The relationship between velocity distributions and topography indicates that compression in the east–west direction, which can lead to crustal and mantle shortening, may play a crucial role in shaping topography. The various structures between the Qinling mountain range in the west and the Tongbai–Dabie mountain range in the east highlight distinct geological processes occurring along the Qinling–Dabie orogenic belt. In addition, we propose that reservoir water has infiltrated the crystalline basement rock layer up to 15–20 km beneath the Three Gorges Reservoir. Furthermore, the Three Gorges Dam serves as a solid foundation to prevent reservoir water from seeping downstream underground.

Use Efficiency, Reduction Potential, and Effects of Fertilizers on Carbon Emissions in China’s Major Citrus Regions

Enhancing the efficiency of fertilizer utilization and advancing fertilizer reduction efforts constitutes a pivotal initiative for augmenting the quality and productivity of the citrus industry; this constitutes an indispensable prerequisite for attaining green and sustainable development. Utilizing panel data from seven prominent mandarin-producing regions and seven prominent tangerine-producing regions in China spanning from 2002 to 2022, this study employed the stochastic frontier analysis (SFA) method to develop a translog production function model for precisely measuring the fertilizer use efficiency for mandarins and tangerines. Employing the calculated optimal fertilizer use rates, we further ascertained the fertilizer reduction potential for mandarin and tangerine; then, we estimated the associated carbon emission reduction potential within these key citrus regions. The research revealed the following findings: the overall level of citrus fertilizer use efficiency in China is comparatively low, with the mean values for mandarin and tangerine fertilizer use efficiency being merely 0.4403 and 0.3887, respectively, indicating substantial room for improvement by approximately 60%; substantial potential exists for decreasing fertilizer use in China’s citrus industry, with average reduction potentials of 66.27% for mandarins and 64.83% for tangerines, signifying a notable redundancy in fertilizer application within major citrus-producing areas. The magnitude of carbon emission reduction potential through the diminution of citrus fertilizer use is tremendous. When optimal fertilizer rates are applied, the average carbon emission reductions resulting from fertilizer reduction in mandarins and tangerines amount to 815.8681 kg/hm2 and 602.3551 kg/hm2, respectively. The average carbon reduction potential for mandarins and tangerines reach levels of 55.9673% and 61.1299%, respectively, both surpassing the threshold of 55%. Significant differences exist in the technical efficiency of fertilizer input, reduction potential, and carbon emission mitigation potential among major citrus-producing regions. Citrus orchards in Guangdong exhibit higher potential for fertilizer reduction but demonstrate a relatively low level of technical efficiency. In contrast, Hunan Province shows an opposite trend, necessitating the development of region-specific strategies. Therefore, to minimize citrus fertilizer use and augment the technical efficiency of citrus fertilizer, it is imperative to comprehensively integrate and promote the “three new” technologies aimed at reducing fertilizer use and enhancing its efficiency within the citrus industry; implement a regional coordinated development strategy for citrus fertilizer reduction; and intensify policy guidance, publicity, and training efforts related to citrus fertilizer reduction, efficiency enhancement, and carbon emissions reduction.

Soil Acidification and Its Temporal Changes in Tea Plantations of Guizhou Province, Southwest China

ABSTRACTSoil acidification is a significant problem in intensive agricultural systems, particularly in tea plantations. Guizhou province, located in the center of the karst mountainous region of southwest China, has the largest tea plantation area in China. However, the soil acidification characteristics in the tea plantations of Guizhou province, as well as their temporal changes, have not yet been adequately studied. Therefore, soil acidification and associated soil properties in the major tea‐planting counties of Guizhou province were extensively assessed, and the temporal changes in soil acidity over the past 11 years were also evaluated through long‐term and short‐term experiments. The results showed that the average soil pH in the tea plantations in Guizhou province varied from 4.08 to 4.87, with a mean of 4.47. The average cation exchange capacity (CEC), TEA (total exchangeable acidity, TEA), EH (exchangeable H+), and EA (exchangeable Al3+) values were 9.6, 3.7, 5.8, and 19.0 cmol/kg, respectively. Seasonal changes in tea plantation soil pH were observed in low‐ and moderately acidic soils but not in highly acidic soils. The soil pH values did not significantly change during three consecutive years of in situ observations but significantly decreased over the past 11 years at an average of 0.03 yearly. Correlation analysis revealed that the soil pH in tea plantations was significantly positively correlated with the content of specific amino acids, such as glutamic acid, arginine, and valine, but was significantly negatively correlated with certain soil physicochemical properties. The findings of this study indicate that soil acidification in tea plantations of the Guizhou province is severe and possesses distinctive attributes that should be considered for its effective management.

A Novel Phenology-Based Index for Plastic-Mulched Farmland Extraction and Its Application in a Typical Agricultural Region of China Using Sentinel-2 Imagery and Google Earth Engine

Plastic-mulching technology has a crucial role to play in modern agriculture by optimizing crop growth environments and enhancing yields. Accurately detecting and mapping the distribution of plastic-mulched farmlands (PMFs) is essential for improving both agricultural management and production efficiency. By analyzing the temporal spectral characteristics of PMFs and crop phenological information, we developed a phenology-based plastic-mulched farmland index (PPMFI). This index, when combined with Sentinel-2 imagery and an automated high-precision extraction process via the Google Earth Engine platform, effectively distinguishes PMFs from other land cover types, especially in complex agricultural landscapes. Validation across areas varying in their background complexity and PMF coverage demonstrated that the proposed PPMFI consistently achieves an overall accuracy rate that exceeds 90%, showcasing its robust performance and significantly outperforming other comparative extraction methods. Applying the PPMFI to the Yudong agricultural region of Henan Province, China, further confirmed its capability for large-scale PMF monitoring, thereby offering critical technical support for sustainable agricultural management and environmental protection.

Research on Ozone Pollution Characteristics and Source Apportionment During the COVID-19 Lockdown in Jilin City in 2022

The increasing Ozone (O3) concentration in various regions of China has garnered significant attention, highlighting the need to understand the mechanisms of O3 formation. This study focuses on the source apportionment of O3 in Jilin City during and after the COVID-19 lockdown countermeasure, and also the influence of anthropogenic emissions on O3 concentration. The contributions of different O3 emission sources were quantified using the Weather Research and Forecasting Community Multi-Scale Air Quality (WRF-CMAQ) model in conjunction with the Integrated Source Apportionment Method (ISAM). The results indicate a significant increase in O3 concentrations during the lockdown in Jilin City, which were particularly characterized by long-distance transportation. Transportation is identified as the primary direct source of O3 in Jilin City, with Yongji County contributing the most among the six designated regions. This study highlights variations in the causes and sources of O3 pollution among the different regions of Jilin City. Simply controlling anthropogenic emissions is inadequate for effectively managing O3 pollution and may even worsen the situation. It is more effective to focus on controlling O3’s precursors. These findings improve the understanding of O3 pollution in Jilin City and provide valuable insights for developing O3 control policies. Similarly, this research is applicable to other countries and regions.

Projecting dry-wet abrupt alternation across China from the perspective of soil moisture

Under a warmer climate, the enhancement of dry-wet abrupt alternation (DWAA) risk poses a great challenge for sustainable development. Here, we introduce a novel framework for DWAA detection based on our proposed soil moisture concentration index. By the end of this century, over humid southern China, the shift of soil moisture time series from anomalously wet to anomalously dry pattern, or the other way around, will be more abrupt. In addition, the proposed framework driven by Coupled Model Intercomparison Project Phase 6 simulations projects more widespread DWAA-affected areas over southwestern China, coastal regions of southeastern China, and the lower reaches of the Yangtze River, especially under a high emission scenario. The framework proposed in this study provides an efficient system for DWAA detection and prediction, and the findings of this study provide a reference for upgrading hydraulic infrastructure and mitigating future DWAA events.

Mining interpretable fuzzy If-Then linguistic rules from energy and economic data to forecast CO[formula omitted] emissions of regions in China

Forecasting CO2 emission is the one of most important issues for the “30⋅60” CO2 emission target in China. Due to unbalanced socio-economic developments of regions in China, exactly forecasting CO2 emissions of provinces depend on their energy consumptions and economic developments. In the paper, a novel method based on K-means clustering method and computing with words is proposed to forecast CO2 emissions of 30 provinces, which is consisted by (1) K-means clustering method is used to respectively cluster energy consumption and economic datasets of provinces and the interpretable fuzzy If-Then linguistic rules of CO2 emissions are mined from the clusters; (2) computing with words method is utilized to transform fuzzy If-Then linguistic rules into fuzzy If-Then rules with membership functions on the universe of discourse; (3) a fuzzy inference method is adopted to forecast CO2 emissions of 30 provinces. To show the usefulness and effectiveness of the novel method, energy consumptions and economic datasets of 30 provinces from 1997 to 2021 are employed to forecast CO2 emissions, metrics of MAE, MAPE, RMSE and the out-of-sample Roos2 are utilized to evaluate CO2 emission forecasting of 30 provinces, means of them reach 13.304, 15.279, 0.081 and 0.965. By comparative analysis for forecasting CO2 emissions of 30 provinces, means of MAE, MAPE, RMSE and the out-of-sample Roos2 by the novel method are more than SVM, ANFIS and MLR methods. In addition, four kinds of mechanisms influencing CO2 are discovered and analyzed by the fuzzy If-Then linguistic rules of 30 provinces, which can help to improve energy consumption and sustainable development of 30 provinces in China.

The analysis of a causal model of learning organization on sustainable organizational performance of public IT companies in China: Knowledge management practices and innovation capability as multiple mediators

This research aims to empirically examine the role of learning organization practices in enhancing sustainable organizational performance, utilizing knowledge management and innovation capability as mediating variables. The study was conducted in public IT companies across China, which is a vital sector for driving innovation and economic growth. A mixed-methods approach was employed, with quantitative methods accounting for 70% and qualitative methods for 30% of the research. Purposive sampling was utilized to distribute questionnaires to 546 employees from 10 public IT companies. Statistical analysis was conducted using Structural Equation Modeling (SEM). The findings indicate that learning organization practices significantly influence knowledge management practices (β = 0.785, p < 0.001) and innovation capability (β = 0.405, p < 0.001). Furthermore, knowledge management practices positively contribute to sustainable organizational performance (β = 0.541, p < 0.001), while innovation capability also has a positive effect (β = 0.143, p < 0.001). Moreover, knowledge management practices partially mediate the relationship between learning organization practices and sustainable performance, with a total effect of 0.788 (p < 0.001). The mediating role of innovation capability is also significant, with a total effect of 0.422 (p = 0.045). The study further includes qualitative in-depth interviews with 20 managers from 10 IT companies across five regions in China: East, South, West, North, and Central. Senior managers were selected through a stratified sampling method to ensure comprehensive representation by including both the largest and smallest companies in each region. These findings underscore the critical role of learning organizations in promoting sustainability through effective knowledge management and innovation capabilities within the IT sector.

Analysis and countermeasures of asymmetric failure in layered surrounding rock tunnels based on FDEM: A case study

In recent years, deep tunnel projects in many regions of China have frequently encountered severe asymmetric failure issues in layered rock structures when crossing through such strata. The unpredictability of the direction and extent of these failures has posed significant challenges for subsequent support design. This paper investigates the asymmetric failure mechanisms of layered rock masses in the Hutou Beishan Mega Tunnel using the Finite-Discrete Element Method (FDEM). First, the smeared method within the FDEM framework is employed to analyze crack evolution, stress states, and asymmetric failure characteristics during tunnel excavation, with the simulation results being compared against field observations for validation. Subsequently, the effects of layer thickness, bedding angle, lateral pressure coefficient, and rock mechanical properties on the asymmetric failure of layered rock masses are systematically explored. The simulation results indicate that the bedding angle and lateral pressure coefficient are the primary factors contributing to the unpredictability of the direction and extent of asymmetric failure in layered rock masses. Based on the analysis of the failure characteristics of these rock masses, the study classifies asymmetric failure into three typical types, providing valuable reference points for predicting the zones and depths of concentrated damage after excavation and for optimizing tunnel support design.

Geographical classification of population: Analysis of amino acid in fingermark residues using UHPLC-QQQ-MS/MS combined with machine learning

ObjectiveTo determine the living regions of individuals based on amino acids in fingermark residues and to establish a rapid and accurate regional classification method using machine learning. Methods: A total of 71 fingermark donors from six different provinces in various regions of China were selected. The content of 18 amino acids in their fingermarks was detected using UHPLC-QQQ-MS/MS. Classification models were established using various machine learning algorithms, and the cross-validation accuracy of 72 combinations, including feature engineering, classification algorithms, and optimization algorithms, was compared. Results: UHPLC-QQQ-MS/MS successfully quantified 16 amino acids. Significant differences in the relative content of amino acids were found between the fingermarks from the eastern and western regions of China, as well as among neighboring provinces. The combination of SFS+SVM+BO was identified as the optimal classification model, achieving an accuracy of 90.14%. Conclusion: The study found regional differences in the relative content of amino acids in fingermarks and established a regional classification method combining UHPLC-QQQ-MS/MS and machine learning. The method developed in this study can be applied to incomplete or distorted fingermarks, and the experimental results can be directly used in police investigations. This research uncovers the multidimensional information carried by fingerprint substances, demonstrating innovation and application value. It not only saves and shortens investigation time and provides investigative leads, but also enables previously unusable physical evidence to play a role again, enhancing the profiling of suspects.

Discriminate shiitake mushroom (Lentinus edodes) from different origins by lipidomics based on UHPLC-QE HR-AM/MS/MS

Shiitake mushroom (Lentinus edodes), as a prestigious edible mushroom and one of the most famous medicinal mushrooms, is cultivated in several regions of China. In this study, the total lipids of shiitake mushroom from different origins were characterized by ultra-high-performance liquid chromatography-quadrupole-Exactive-Orbitrap MS/MS (UHPLC-QE Orbitrap/MS/MS). Twenty lipids and 174 lipid molecular species were determined by non-targeted lipodomics analysis. The multivariate analysis showed that 19 lipids were screened as potential markers for distinguishing shiitake mushroom of different origins, for instance, phosphatidic acid (18:2/18:2) (PA 18:2/18:2) and phosphatidylglycerol (16:0/18:2) (PG 16:0/18:2). And the Partial least squares discriminant analysis (PLS-DA) model reconstructed was used to distinguish shiitake mushroom from different regions with a success rate of 100%. This work comprehensively provided the lipid information of shiitake mushroom and demonstrated the great potential of authenticity traceability of shiitake mushroom from different origins from the perspective of lipidomics.

Regional and longitudinal dynamics of human milk protein components assessed by proteome analysis on a fast and robust micro-flow LC–MS/MS system

An in-depth exploration of molecular composition of human milk could provide a scientific basis for the development of substitutes. The present study was conducted to analyze human milk proteins from 110 individuals from five regions of China and across three stages of lactation to investigate the change patterns. We developed a micro-flow liquid chromatography tandem mass spectrometry (μLC–MS/MS) system with data-independent acquisition (DIA) proteomics technology that can rapidly and stably characterize the human milk proteome. In total, 2796 proteins were identified. Among these proteins, CPM, ACSL1, and RPL13 changed significantly during lactation, and SCP2, GALK1 and GALE changed significantly between regions. Bioinformatics analysis revealed that human milk is altered by complex interactions between genetic and environmental factors. Our results not only reveal the regional and longitudinal patterns of change in human milk proteome but also provide theoretical basis and technical support for the production and quality control of infant formula.

Digital transformation, audit risk, and the low-carbon transition of China's energy enterprises

While digital transformation is recognized for driving efficiency and sustainability in China's energy sector, its impact on low-carbon transitions through the lens of audit risk remains underexplored. This study examines how digital transformation, encompassing technologies like AI, IoT, Big Data, and blockchain, affects the low-carbon transition of Chinese energy enterprises, with a specific focus on the mediating role of audit risk. Using data from 2008 to 2022, we find that digital transformation significantly enhances low-carbon transitions. Furthermore, our analysis reveals that audit risk, particularly abnormal expenses, mediates this relationship, with lower audit risk amplifying the positive effects of digital transformation. This underscores the importance of robust audit and risk management practices in ensuring the effectiveness of digital transformation for achieving sustainability goals. Importantly, our findings highlight key heterogeneities in this relationship: the impact of digital transformation varies across regions in China and is influenced by the type of audit firm. This suggests that the Eastern region and firms audited by Non-Big Four firms experience the most significant benefits from digitalization in their pursuit of low-carbon transitions. These insights offer valuable guidance for energy companies and policymakers navigating the complexities of digital innovation and sustainable development in China's energy sector.

A novel method for tracing gasoline using GC-IRMS and Relief-Stacking fusion model

Due to China’s vast geographical expanse and the complexity of its natural environment, traditional methods are not suitable for tracing the origins of gasoline samples from different regions of China. The aim of this study is to trace the origins of gasoline samples through the analysis of carbon stable isotopic characteristics from 60 gasoline samples collected across four major regions in China. In this study, a novel method that combines gas chromatography-isotope ratio mass spectrometry (GC-IRMS) with Relief-Stacking was proposed for the first time, significantly enhancing the precision of the provenance results. Firstly, eight characteristic components were identified by Relief feature selection model for the first time and applied to the assessment of carbon stable isotope ratios across different regions. The discovery of these components effectively reduces data redundancy, thereby enhancing the accuracy of provenance analysis. Subsequently, five effective source tracing models (XGBoost, Random Forest, CatBoost, LightGBM, and KNN) were selected based on the CEI values. To further enhance the learning efficiency of the sourcing models, this study integrated the five selected source tracing models using three different methods(voting, stacking, and blending) and validated the performance of the optimal sourcing model. The results indicated that the Stacking fusion model achieves a CEI value of 0.923, significantly outperforming other fusion models. This advantage is attributed to the Stacking fusion model combining the s trengths of Boosting and Bagging ensemble methods and leveraging the advantages of individual classifiers within the model. The method proposed in this study can effectively address the limitations of previous tracing methods and offer valuable insights for the development of platforms related to stable isotope source tracing. In future research, through appropriate parameter adjustments and feature adaptations, the Relief-Stacking model can likewise be applied to source tracing studies in other countries characterized by extensive geographic ranges and variable environmental conditions.

Integrity-centered framework for determining protected areas boundary: An application in the China's national park

National parks are critical components of protected areas and are drawing increased global attention. Clear and rational boundaries may provide a scientific foundation for the sustainable protection and management of these areas. However, criteria for feasible national park delimitation and techniques for resolving conflicts between socioeconomic development and environmental protection are still poorly defined, particularly regarding integrity during park establishment. Consequently, a systematic analytical framework is essential for advancing research and practice. This study developed a systematic framework to determine the spatial boundaries, using the Kunlun Mountain region in China as a case for empirical analysis. Key issues in national park development—ecology and management—were systematically integrated into this framework. Most importantly, spatial connectivity, a critical concern in national park planning, was thoroughly considered for improving integrity optimization, an aspect often overlooked in previous delimitation studies. The results demonstrated that our framework effectively identified potential national park boundaries, achieving a balance between development and conservation. Furthermore, core protected and general control areas were delineated to support differentiated management approaches. This framework offers a scientific foundation for policymakers and managers to pursue sustainable protection and management of national parks.

Lithospheric magnetic structure of cratonic regions in Central-Eastern China inferred from aeromagnetic anomalies: Insights into magnetization in the uppermost mantle

Most of the subcontinental lithospheric mantle (SCLM) beneath Proterozoic cratons consists of refertilized Archaean SCLM. Variations in SCLM composition and its physical properties significantly affect the stabilization and preservation of the ancient continents. In this paper, aeromagnetic data are analyzed to reveal the magnetic structure of the lithospheric mantle beneath two major Precambrian blocks in central-eastern China, i.e., the Upper Yangtze Block (UYB) and Ordos Block (OB). After being reduced to the pole, the Fourier power spectrum of the aeromagnetic anomalies is calculated to determine the depth to magnetic sources. Considering the lower spatial resolution of the power spectral analysis in dealing with the long-wavelength aeromagnetic anomalies, we applied the scale-normalized continuous wavelet transform (CWT) on the magnetic data to trace the magnetic sources, with special focus on deeper ones. Synthetical model of a magnetic layer and application to the profile data validate the effectiveness of this scale normalization scheme in improving the wavenumber/spatial resolution in the CWT scalogram.In order to present a detailed magnetic structure, we carried out 2.5D forward modeling work on the magnetic data of a 2280 km-long nearly N-S profile across the UYB and OB. Due to the inherent ambiguity in the modeling results, the CWT-based spectral analysis is successfully adopted to provide source depth constraints for the initial model. The constrained forward modeling results indicate strong inhomogeneities among main tectonic blocks of studied area, like humans have different fingerprints. The magnetization of OB is larger than that of UYB since its Archean to Paleoproterozoic metamorphic basement are widely exposed at the surface, while the Precambrian basement of UYB is mostly overlain by unmetamorphosed Sinian cover and weakly metamorphosed Neoproterozoic strata. The most interesting aspect is that deep-seated magnetic sources might reside in the uppermost mantle of UYB and OB, suggesting vertical layering in the SCLM in cold cratonic regions.

Stronger biogeographical pattern of bacterioplankton communities than biofilm communities along a riverine ecosystem: a local scale study of the Kaidu river in the arid and semi-arid northwest of China

Although the biogeographical pattern and mechanisms underlying microbial assembly have been well-explored in lentic ecosystems, the relevant scenarios in lotic ecosystems remain poorly understood. By sequencing the bacterial communities in bacterioplankton and biofilm, our study detected their distance-decay relationship (DDR), and the balance between deterministic and stochastic processes, along the Kaidu river in an arid and semi-arid region of northwest China. Our results revealed that bacterioplankton and biofilm had significantly contrasting community structures. The bacterioplankton communities showed a gradually decreasing trend in alpha-diversity from the headwater to the river mouth, contrasting with the alpha-diversity of biofilm communities which was constant along the river length. Both bacterioplankton and biofilm showed significant DDRs along the 500-km river corridor with the slope of the bacterioplankton DDR being steeper than that of the biofilm DDR, which implies a stronger biogeography of bacterioplankton than biofilm. Relative to biofilm communities, the species interactions formed a denser and more complex network in the bacterioplankton communities than in the biofilm communities. Our results also revealed that there was a transition of community assembly from deterministic to stochastic processes upstream to downstream, although both the bacterioplankton and biofilm communities were mainly regulated by deterministic processes within the entire river. All these empirical results expand our knowledge of microbial ecology in an arid and semi-arid lotic ecosystem.

Differentiated growth of the most widely planted conifer in response to extreme droughts across semi-arid regions in Northern China

Global warming is leading to more frequent and intense drought events, exerting unprecedented pressure on forest growth. Although post-drought recovery in plantation growth has been studied enormously, the variation of planted populations across the whole distribution range of a species is not well understood. In this study, the growth suitability of the most widely planted conifer species in dry lands of northern China, Mongolian Scots pine (Pinus sylvestris var. mongolica), was estimated, based on tree-ring data sampled in 289 plots of afforestation across most of its planting range. According to changes in the hydrothermal condition during the last forty years (1980–2018), the distribution range was divided into three parts: warming, warming-wetting, and non-significant trends (NT) regions. Two severe droughts in 2000 and 2007 were identified using the Standardized Precipitation Evapotranspiration Index (SPEI). Then, the resistance (Rt), recovery (Rc), and resilience (Rs) growth indices were calculated and compared among these three parts. The growth of planted Mongolian Scots pine has overall significantly increased from 1980 to 2018 (p < 0.001). The pine plantations in warming regions were sensitive to annual precipitation, while warming-wetting regions were more influenced by the growing season climate. Pines reverted to pre-drought growth levels within 3 years after the drought event in 2000 but failed in 2007, and populations in the warming region showed significantly lower post-drought recovery. Furthermore, high pre-drought growth levels were linked to low Rs in both drought events (p < 0.001). Post-drought precipitation enhanced the Rc of plantations, compensating for the growth reduction caused by droughts. Soil moisture during the drought events enhanced Rt and Rs. The Rt and Rs indices decreased significantly with the age of plantations (p < 0.05). Overall, our results revealed that pre-drought growth and post-drought precipitation were key factors influencing the resilience of planted Mongolian Scots pine. Besides, the plantations experiencing wetting trends revealed favorable drought resilience and stable radial growth, which indicated promising potential for planted Mongolian Scots pine in those areas. Controlling the age of plantations seems to be crucial for maintaining drought resilience in large-scale plantations and maximizing ecological benefits.